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Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning
Jin Jia1, Mingming Liu2, Zhigang Xue1
1Information Center, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, P.R. China.
Background:
We aimed to compare the prediction efficiency of back propagation (BP) network and grey model (GM) (1.1) for mumps infectious diseases and compare the application effect of the two models.
Methods:
By calculating the average incidence rate of mumps in January 2014 -2016, we conducted the modeling of the BP time series, GM (1,1) grey model and the combination models of them, and predicted the incidence rate in June 2016 in comparison with the actual one. We compared the quarterly incidence rate to test the two prediction models, and compared the advantages and disadvantages of these models.
Results:
R value of BP model was 68.45%, for GM (1,1) was 58.49%, and for combined forecasting model was 86.95%. We used the principal component analysis clustering method to control the samples, and found that the samples were close to the population mean. We found that the GM (1.1) model was more suitable for the prediction of mumps infection mode. We carried out dimension reduction analysis on the model data, and the accuracy of the data after dimension reduction is within the range of Da. For the discrete degree of the data in the combined model, matlab pipeline was used to verify the reliability of the data and results. By calculation after manifold optimization small error probability was P=0.875 and semi mean relative error 2.43%.
Conclusion:
BP, GM (1,1) is a better method for modeling the epidemic trend of mumps in China, but the efficiency of prediction is not as high as the combination of them.
Insights
A combined forecasting model integrating back propagation (BP) and grey model (GM) (1,1) significantly improved mumps infectious disease prediction accuracy. This hybrid approach offers superior efficiency for modeling epidemic trends compared to individual models.
Area of Science:
- Epidemiology
- Infectious Disease Modeling
- Biostatistics
Background:
- Mumps remains a significant public health concern requiring accurate prediction models.
- Existing models like back propagation (BP) and grey model (GM) (1,1) have limitations in predicting infectious disease outbreaks.
Purpose of the Study:
- To compare the predictive efficiency of BP networks and GM (1,1) for mumps infectious diseases.
- To evaluate the application effectiveness of these individual models and their combination.
Main Methods:
- Calculated average mumps incidence rates from January 2014-2016.
- Developed time series models using BP, GM (1,1), and a combined approach.
- Predicted incidence rates for June 2016 and compared with actual data.
Main Results:
- The combined model achieved the highest R value (86.95%), outperforming BP (68.45%) and GM (1,1) (58.49%).
- Principal component analysis confirmed sample proximity to the population mean.
- GM (1,1) showed suitability for mumps infection prediction, with the combined model demonstrating high data reliability and low error rates (P=0.875, semi mean relative error 2.43%).
Conclusions:
- Both BP and GM (1,1) are viable for modeling mumps epidemic trends in China.
- The combination of BP and GM (1,1) offers significantly enhanced prediction efficiency over individual models.
